Robust extended Kalman filtering for camera pose tracking using 2D to 3D lines correspondences

International audienceIn this paper we present a new robust camera pose estimation approach based on 3D lines tracking. We used an Extended Kalman Filter (EKF) to incrementally update the camera pose in real-time. The principal contributions of our method includes first, the expansion of the RANSAC scheme in order to achieve a robust matching algorithm that associates 2D edges from the image with the 3D line segments from the input model. And second, a new framework for camera pose estimation using 2D-3D straight-lines within an EKF. Experimental results on real image sequences are presented to evaluate the performances and the feasibility of the proposed approach

3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel Map

This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside each object is performed based on the voxel’s geometric class. The work here evaluates the proposed algorithm in various versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which is novel.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Outdoor Augmented Reality: State of the Art and Issues

International audienceThe goal of an outdoor augmented reality system is to allow the human operator to move freely without restraint in its environment, to view and interact in real time with geo-referenced data via mobile wireless devices. This requires proposing new techniques for 3D localization, visualization and 3D interaction, adapted to working conditions in outdoor environment (brightness variation, features of displays used, etc.). This paper surveys recent advances in outdoor augmented reality. It resumes a large retrospective of the work carried out in this field, especially on methodological aspects (localization methods, generation of 3D models, visualization and interaction approaches), technological aspects (sensors, visualization devices and architecture software) and industrial aspects

Toward a Real-Time 3D Reconstruction System for Urban Scenes Using Georeferenced and Oriented Images

International audienceThis paper presents an original 3D reconstruction technique for modeling urban scenes. Such modeling can lead many useful applications: virtual navigation, augmented reality, architectural planification, etc. Our approach uses a hybrid sensor combining a vision-inertial system and a GPS receiver to explore the urban environment and to store interesting data. Each captured image is annotated according GPS position and inertial sensor orientation and used iteratively for 3D reconstruction. This approach allows a metric reconstruction of the scene and thus solves the problem of the scale factor met in the projective reconstruction. Another contribution of our method is about the development of a robust markerless tracking approach. Natural and distinctive image features of the scene are detected and tracked frame-to-frame using the SIFT descriptors. We demonstrate the effectiveness of our approach through experiments on real data in outdoor environment

A robust method for 3D hand tracking using histogram of gradients and particle filter

International audienceThis work deals with 3D hand tracking in cluttered background which is an important task in human computer interaction and non intrusive marketing behavior. In this paper a particle filter framework is proposed to integrate gradient distributions and image observations in order to estimate the 3D position of hand from monocular image sequences. Extensive experiments have bee carried out to demonstrate the efficiency and the robustness of our approach

Efficient particle filtering using patch matching for real-time self localization

International audienceIn this paper we present a novel approach for 3D camera self localization, that uses template matching in a particle filter framework. We propose a tracking scheme that exploits the matching scores between image patches to design the likelihood function of the filter observation model. Indeed, by representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Experiment results on tracking a hand-held camera have shown that the proposed approach provides more accurate tracking, especially for fast motion or long-term partial occlusions. Comparisons have been made with existing methods; results have shown that the proposed scheme has provided an improved tracking accuracy at the cost of more computations

Robust Camera Egomotion Estimation from 3D Straight Line-Based Environment model

International audienceIn this paper we present a new robust camera pose estimation approach based on 3D lines features. The proposed method is well adapted for mobile augmented reality applications. We used an extended Kalman filter (EKF) to incrementally update the camera pose in real-time. The principal contributions of our method include first, the expansion of the RANSAC scheme in order to achieve a robust matching algorithm that associates 2D edges from the image with the 3D line segments from the input model. And second, a new powerful framework for camera pose estimation using only 2D-3D straight-lines within an EKF. Experimental results on real image sequences are presented to evaluate the performances and the feasibility of the proposed approach in indoor and outdoor environments

Comparative study of marker-based camera tracking using extended and unscented Kalman filters

International audiencecamera pose tracking is an important issue for several modern applications like augmented reality, surveillance systems, robot localization, etc. In this paper, we compare the performances of unscented and extended Kalman filtering for improving camera pose estimation. For that, we propose to use external square markers to provide an accurate motion measurements. Several experiments are achieved in order to compare the accuracy of the two filters. Our results show that, for this kind of applications, the extended Kalman filter (EKF) can perform better than an unscented Kalman filter (UKF) but at a much lower computational cost

Advanced 3D localization by fusing measurements from GPS, inertial and vision sensors

International audienceIn this paper we present an efficient algorithm for estimating the 3D localization in an urban environments by fusing measurements from GPS receiver, inertial sensor and vision. Such hybrid sensor is important for numerous applications including outdoor mobile augmented reality and 3D robot localization. Our approach is based on non-linear filtering of these complementary sensors using a multi-rate Extended Kalman Filter. Our main contributions concern the modeling of the sensor fusion and the development of an efficient approach for camera pose tracking using only natural features. This method improves the accuracy of the estimated 3D localization. We evaluated the performances of our approach and demonstrated its effectiveness through experiments on real data

Real-time camera tracking for structured environment using an iterated particle filter

International audienceIn this paper, we present a new solution for the problem of tracking the 3D pose of a hand held camera using particle filtering framework. The proposed approach utilizes a 3D straight-line based model of a structured environment. The principal novelty is in the use of the 2D edges from the image and the 3D lines from the input model within the particle filter, especially to design the likelihood function of the filter observation model. Results from real data are presented, demonstrating the efficiency and robustness of the proposed method